Genetic Variability, Correlation and Path Analysis for Yield and Yield Components in Soybean

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1 American-Eurasian J. Agric. & Environ. Sci., 15 (2): , 2015 ISSN IDOSI Publications, 2015 DOI: /idosi.aejaes Genetic Variability, Correlation and Path Analysis for Yield and Yield Components in Soybean M.M. Mahbub, M. Mamunur Rahman, M.S. Hossain, F. Mahmud and M.M. Mir Kabir 1 Scientific Officer, Agronomy Division, Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh 2 Senior Scientific Officer, BRRI, Regional Station-Satkhira, Bangladesh 3 Department of Genetics and Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh 4 Scientific Officer, Entomology Division, BRRI, Gazipur, Bangladesh Abstract: Twenty eight soybean genotypes were evaluated for eleven morphological characters during Robi season, 2011 at Sher-e-Bangla Agricultural University, Dhaka, Bangladesh. Analysis of variance revealed significant differences among the genotypes for all the traits. Plant height, seed yield per plant, branches per plant, number of seeds per pod, number of pods per plant and hundred seed weight exhibited that they were controlled by additive gene action and selection for the improvement of these traits would be rewarding. The phenotypic variance was higher than the corresponding genotypic variance for all the characters. All the characters showed moderate to low phenotypic and genotypic coefficient of variation. Genotypic coefficient of variation was the highest for seed yield per plant (31.45%) followed by number of branches per plant (29.9%) and plant height (27.42% ). Days to maturity (99.93%) had the highest heritability. Plant height, pod length, number of seeds per pod, number of pods per plant, hundred seed weight, branches per plant and number of seeds per pod showed significant positive genotypic and phenotypic correlation with seed yield. In path analysis, seeds per pod, hundred seed weight, pod length, days to maturity and plant height showed positive direct effect on yield. Considering genetic variability, correlation and path analysis, emphasis should be given on plant height, pod length, seeds per pod and hundred seed weight during breeding program to improve seed yield of soybean. Key words: Soybean Diversity Correlation Path coefficient analysis INTRODUCTION oil as a source of energy [2]. Because of a good source of protein, unsaturated fatty acids, minerals like Ca and P Soybean (Glycine max (L.) Merrill) is a wonderful including vitamin A, B, C and D, soybean can meet up crop gifted by the nature to mankind which is one of the different nutritional needs [3]. Soy protein products can richest sources of oil as well as protein. It belongs to the be good substitutes for animal products because, family Leguminosae and is a self-pollinated crop having soybean offers a 'complete' protein profile and can chromosome number of 2n=40. Its seed contains about effectively replace animal-based health hazardous 40 to 42% protein and 18 to 22% oil [1]. The present foods [4]. Moreover soybean also contain numerous nutritional situation of third world and some developing compounds that act as antioxidant and are beneficial to countries like Bangladesh is a matter of great concern human health as they diminish the risk of cardiovascular since the most of the people are suffering from diseases, breast cancer, osteoporosis, diabetes and malnutrition. Soybean can play an important role in this neurodegenerative diseases such as Alzheimer s and case and can help to meet up the nutritional deficiency Parkinson s and reduce the menopausal symptoms [5]. problem. Soybean could be regarded as an ideal food for The low yield of soybean in these countries is the the people of poor and developing countries as it main constrain for growing of soybean. Therefore, contains high quality protein and reasonable quantity of breeders should emphasize on yield improvement of Corresponding Author: Md. Mostofa Mahbub, Scientific Officer, Agronomy Division, BRRI, Gazipur, Bangladesh. Cell:

2 soybean. In order to increase yield, genetic variability is the prerequisites since it is the source of variation and raw material for yield improvement work [6]. Assessment of genetic variability is also needed for efficient parent selection in breeding program [7], long term selection gain and exploitation of heterosis [6]. Furthermore, characters associated with yield are to be determined by correlation and path coefficient analysis to assist selection in yield improvement work. Though correlation analysis indicates the association pattern of component traits with yield, they also represent the overall influence of a particular trait on yield rather than providing cause and effect relationship. The path coefficient analysis technique facilitates the partitioning of genotypic correlation into direct and indirect contribution of various characters on yield [6-9]. Such information would be of great value in enabling the breeder to specifically identify the important component traits of yield and utilize the genetic stock for improvement in a planned way. Mention the objectives of the present study?? MATERIALS AND METHODS The study was conducted to assess the genetic diversity, correlation and path coefficient analysis among twenty eight soybean genotypes (Table 1). The experiment was carried out during December, 2011 to April, 2012 at the field of Sher-e-Bangla Agricultural University, located at N latitude, E longitude at an altitude of 8.6 m above sea level in Dhaka, Bangladesh. Experimental material consisting of twenty eight genotypes were sown in a randomized complete block design with three replications; each plot consisted of a single row of 3 m long with row to row distance of 50 cm maintaining 10 plants per meter. Sowing was done with the help of a hand drill. Ten random plants were used to take the data on days to first flowering, days to 50% flowering, days to maturity, plant height, number of branches per plant, number of pods per plant, pod length, number of seeds per pod, seeds per plant, hundred seed weight and seed yield per plant from each plot of each replication. All cultural operations were carried out following standard procedures as described by Mondal et al. [10]. The data were analyzed by MSTAT program for ANOVA. Phenotypic variances, genotypic variances and error variances were estimated following Johnson et al., [11]. Genotypic and phenotypic coefficients of variation were estimated according to Burton [12]. Table 1: Name of twenty eight soybean genotypes used in the present study. Sl. No. Genotypes No. Name 1 G1 AGS-79 2 G2 BS-13 3 G3 JOYAWAZA 4 G4 F G5 AGS-95 6 G6 GC G7 BOAS-5 8 G8 LG-92P G9 BS G10 GC G11 ASSET G12 BARI SOYBEAN-6 13 G13 KANH G14 NS-1 15 G15 GMOT G16 LG-92P G17 CHINA-1 18 G18 MTD G19 SHOHAG 20 G20 PK G21 AUSTRALIA 22 G22 YESOY-4 23 G23 PI G24 MTD G G26 ASSET G27 BARI SOYBEAN-5 28 G28 MTD-451 Source: Bangladesh Agricultural Research Institute (BARI) Broad sense heritability, genetic advance (GA) and genetic advance in percent of mean (GAPM) were estimated using the formula suggested by Johnson et al., [11] and Hanson et al., [13]. Genotypic and phenotypic correlation coefficient were carried out using formula suggested by Miller et al., [14], Johnson et al., [11] and Hanson et al., [13]. The correlation coefficients were further partitioned into components of direct and indirect effects by path coefficient analysis developed by Wright [15] and later described by Dewey and Lu [16]. RESULTS AND DISCUSSION Genetic Variation: All the morphological traits showed highly significant variations among the genotypes (Table 2). The phenotypic variance was slightly higher than the corresponding genotypic variance for all the characters and showed moderate to low phenotypic and 232

3 genotypic coefficient of variation. High phenotypic and and high genetic advance in percent of mean of these genotypic variances with low environmental variance for traits showed that these traits were under the control of all the characters indicate that the expression of the genes additive gene and selection or the improvement of these controlling the characters are not markedly influenced by traits could be effective. This finding is comparable with the environmental conditions [17]. Environmental the results previously published by Khan [18] and variances were negligible for all the traits except plant Iqbal et al. (2003) [8]. height, number of pods per plant and seeds per pod. These three traits might be influenced in a small extent by Correlation among the Traits: It is evident from Table 3 the environmental factors. The expressions of other traits that the genotypic correlation coefficients were larger in are mainly due to the genetic constituents rather than values as compared to their respective phenotypic environmental influence. The differences between GCV correlation coefficient. This indicates greater contribution and PCV were very low for all characters studied, of genetic factor in the development of the association. indicating that the environmental effects in the Significant positive genotypic and phenotypic development of these parameters are very low. correlations were found between days to first flowering, Environmental coefficient of variation was also low for days to 50% flowering, days to maturity, plant height, branches per plant, number of pods per plant and number number of branches per plant and pod length. Number of of seeds per pod and very low for other traits. Heritability pods per plant showed significant positive correlations was also higher for all traits which also indicate that with plant height, branches per plant, number of seeds per genetic constituents are the main source of these traits. pod, seeds per plant and seed yield. Seed yield showed High genetic advance was found for plant height and the highest significant positive genotypic and phenotypic seed per plant. Genetic advance was moderate for days to correlation with pod length followed by number of seeds first flowering, days to 50% flowering, days to maturity per pod, number of pods per plant, hundred seed weight, and number of pods per plant and low for branches per branches per plant, plant height and number of seeds per plant, plant height, number of seeds per pod, hundred plant. Similar results were recorded by Ghodrati et al., [19]. seed weight and seed yield per plant. Genetic advance Such results indicated that the increase of one character (GA) in percent of mean for seed yield per plant, branches will increase in the correlated character. For example, per plant, plant height, seeds per pod, number of pods per plant height was positively and significantly plant and hundred seed weight were high showing that correlated with number of pods per plant, hence these parameters could be improved in a large extent by taller plants are more likely to produce greater plant selection improvement program. High heritability number of branches and number of pods per plant. Table 2: Estimation of genetic parameters in eleven characters of 28 genotypes in soybean Traits Range Mean MS CV (%) 2p 2g 2e PCV% GCV% ECV% Heritability GA (5%) GA (% mean) DFF ** D50%F ** DM ,101.14** PH ** BPP ** NPP ** PL ** NSP ** SPP ** HSW ** SYP ** ** means correlation is significant at the 0.01 level. [DFF = Days to first flowering, D50%F = Days to 50% flowering, DM = Days to maturity, PH = Plant height (cm), BPP = Branches per plant, NPP = Number of pods per plant, PL = Pod length (cm), NSP = Number of seeds per pod, SPP = Seeds per plant, HSW = Hundred seed weight (g), SYP = Seed yield per plant (g), MS = mean sum of square, CV (%) = Coefficient of variation, 2 p = Phenotypic variance, 2g = Genotypic variance, 2 e = Environmental variance, PCV = Phenotypic coefficient of variation, GCV = Genotypic coefficient of variation, ECV = Environmental coefficient of variation] 233

4 Table 3: Genotypic (G) and phenotypic (P) correlation coefficient among different pairs of yield and yield contributing characters for different genotypes of soybean D50%F DM PH BPP NPP PL NSP SPP HSW SYP DFF G 0.999** 0.917** 0.464** 0.527** ** P 0.985** 0.913** 0.460** 0.509** ** D50%F G 0.916** 0.480** 0.543** ** * P 0.905** 0.467** 0.518** ** DM G 0.411** 0.451** P 0.408** 0.435** PH G 0.865** 0.666** 0.326** ** ** 0.389** P 0.838** 0.640** 0.326** ** ** 0.380** BPP G 0.556** 0.490** ** ** P 0.530** 0.465** ** ** NPP G 0.266** 0.347** 0.991** * 0.664** P 0.255** 0.291** 0.984** * 0.662** PL G 0.300** 0.336** 0.635** 0.749** P 0.256** 0.325** 0.626** 0.727** NSP G 0.388** ** P 0.340** ** SPP G ** P ** HSW G 0.558** P 0.551** ** = Significant at 1%. * = Significant at 5%. [DFF = Days to first flowering, D50%F = Days to 50% flowering, DM = Days to maturity, PH = Plant height, BPP = Branches per plant, NPP = Number of pods per plant, PL = Pod length, NSP = Number of seeds per pod, SPP = Seeds per plant, HSW = Hundred seed weight, SYP = Seed yield per plant ] Table 4: Path coefficient analysis showing direct and indirect effects of different characters on yield of soybean Indirect effect Pearson Correlation Characters Direct effect DFF D50%F DM PH BPP NPP PL NSP SPP HSW with yield DFF D50%F DM PH ** BPP ** NPP ** PL ** NSP ** SPP ** HSW ** Residual effect: ** indicates significant at 1% and * indicates significant at 5% level of probability. [DFF = Days to first flowering, D50%F = Days to 50% flowering, DM = Days to maturity, PH = Plant height, BPP = Branches per plant, NPP = Number of pods per plant, PL = Pod length, NSP = Number of seeds per pod, SPP = Seeds per plant, HSW = Hundred seed weight, SYP = Seed yield per plant] Chand [20] and Rajanna et al., [21] reported similar results and showed positive and significant correlations of plant height with number of pods and number of branches. Therefore, to improve yield of soybean, emphasis should be given on the correlated traits (pod length, number of seeds per pod, number of pods per plant, hundred seed weight, branches per plant, plant height and number of seeds per plant) based on the strength of their correlation. On the other side, hundred seed weight showed significant negative genotypic and phenotypic correlation with plant height and number of pods per plant. It means that increase of plant height and number of pods per plant decreased the seed weight. Khanghah and Sohani [22], Rajanna et al., [21], Singh and Yadava [23] and Arshed et al., [24] also reported similar findings for different parameters in soybean. 234

5 Path Analysis of the Traits: The data presented in 3. Rahman, L., Cultivation of Soybean and Its Table 4 showed that seeds per pod had the maximum positive direct effect on yield (1.20) followed by hundred seed weight (0.739), pod length (0.072), days to maturity (0.062) and plant height (0.043). Similar results were reported by Malek et al., [25]. Number of pods per plant showed the highest negative direct effect on yield (-0.040) followed by branches per plant (-0.031), days to first flowering (-0.010), number of seeds per pod (-0.004) and days to 50% flowering (-0.001). Branches per plant, number of pods per plant and number of seeds per pod had negative direct effect but significant positive correlation with yield. Though number of branches per plant, number of pods per plant and number of seeds per pod showed highly significant positive correlation with seed yield, but they showed negative direct effect on yield. Number of seeds per pod showed very little negative direct effect on yield but showed positive indirect effect on days to 50% flowering, plant height, pod length, seeds per pod and hundred seed weight along with highly significant positive correlation with seed yield. Therefore, this trait could consider for yield improvement. CONCLUSIONS All the morphological traits showed highly significant variations among the genotypes and the variations could be used in plant improvement program. Seed yield per plant, branches per plant, plant height, seeds per pod, number of pods per plant and hundred seed weight were controlled by additive gene action and selection for the improvement of these traits could be effective. Furthermore, among studied traits, four traits viz, plant height, pod length, seeds per pod and hundred seed weight showed highly positive correlation and positive direct effect on seed yield. Therefore, emphasis should be given on these traits during selection in breeding program in order to increase seed yield. REFERENCES 1. Antalina, Recent research and industrial achievement for soybean in Japan. Proceeding of RIELT-JIRCAS. Workshop on Soy Research, Sep. 28, Khaleque, M.A., A guide Book on Production of Oil crops in Bangladesh, DAE, Ministry of Agric. Govt. of Bangladesh and FAO/UNDP Project, Strengthening the Agricultural Extension Service, Khamarbari, Dhaka, pp: Uses. The City Press, Dhaka, pp: Henkel, J., Soy: Health Claims for Soy Protein, Question About Other Components. FDA Consumer (Food and Drug Administration). 34(3): Kumar, A.V., S. Kumar, K. Lal, M. Jolly and A. Sachdev, Influence of gamma rays and ethyl methane sulphonate (EMS) on the levels of phytic acid, raffinose family oligosaccharides and antioxidants in soybean seeds of different genotypes. Journal of Plant Biochemistry and Biotechnology Vol number?.: Rahman, M.M., M.K. Bashar and M.G. Rasul, Molecular Characterization and Genetic Variation in Rice. LAP Lambert Academic Publishing GmbH and Co. KG, Saarbrucken, Germany, pp: Rahman, M.M., M.G. Rasul, M.K. Bashar, M.A. Syed and M.R. Islam, Parent selection for transplanted Aman rice breeding by morphological, physiological and molecular diversity analysis. Libyan Agriculture Research Center Journal International, 2(1): Iqbal, S., T. Mahmood, Tahira, M. Ali, M. Anwar and M. Sarwar, Path-coefficient analysis in different genotypes of soybean [Glycine max (L) Merrill]. Pakistan Journal of Biological Science, 6(12): Akhter, M. and C.H. Sneller, Yield and yield components of early maturing soybean genotypes in the mid-south. Crop Sci., 36: Mondal, M.R.I. and M.A. Wahhab, Production technology of oil crops. Oil seed research centre. Bangladesh Agricultural Research Institute, pp: Johnson, H.W., H.F. Robinson and R.E. Comstock, Estimation of genetic and environmental variability in soybean. Agron. J., 47(7): Burton, G.W., Quantitative interaction in grasses. In: Proc. 6th International Grassland Congress, 1(2): Hanson, C.H., H.F. Robinson and R.E. Comstock, Biometric studies of yield segregating population in Korean lespedeza. Agron. J., 48: Millar, P.A., J.C. Williams, H.F. Robinson and R.E. Comstock, Estimates of genotypic and environmental variance and covariance and their implication in selection. Agron. J., 50: Wright, S., The method of path coefficients. The Annals of Mathematical Statistics, 5(3):

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